Advances in mobile electronic devices have brought about a tremendous expansion in the functionality available to users of the devices. Many mobile devices include technology to make a phone call, browse the internet, determine a current location, or the like. Some mobile electronic devices also include functionality to capture video images using digital camera technology. With increased availability to capture digital images, many developers are considering new ways to leverage digital cameras as an input devices geared toward functionality in addition to merely capturing events for subsequent viewing.
Many developers are now considering new ways for analyzing data captured by digital camera. One mechanism that has been evolving is object recognition. In this regard, a digital image is processed in a manner such that objects within the image may be identified. For example, a user may take a digital image of a city building. Attributes of the building extracted from the image may be compared to a predefined attributes in a database. If an attribute match is found, the object, in this case the building, may be identified. Having identified the object, a variety of functionality may become available relation to the object.
Many conventional solutions for implementing object recognition utilize substantial computing power to extract the image attributes and analyze the digital image. Further, when a digital video feed is considered, which is essentially a series of digital still image frames, the computing power utilized by conventional object recognition solutions can increase accordingly. When the video feed includes motion, conventional solutions may be required to re-analyze each frame of the video anew in order to perform object recognition in order to track the motion of the recognized object.